Real-time detection of respiratory activity using an inertial measurement unit

A novel acquisition platform for long-term breathing frequency monitoring based on inertial measurement units

Continuous monitoring of breathing frequency (f_B) could foster early prediction of adverse clinical effects and exacerbation of medical conditions. Current solutions are invasive or obtrusive and thus not suitable for prolonged monitoring outside the clinical setting. Previous studies demonstrated the feasibility of deriving f_B by measuring inclination changes due to breathing using accelerometers or inertial measurement units (IMU). Nevertheless, few studies faced the problem of motion artifacts that limit the use of IMU-based systems for continuous monitoring. Moreover, few attempts have been made to move towards real portability and wearability of such devices. This paper proposes a wearable IMU-based device that communicates via Bluetooth with a smartphone, uploading data on a web server to allow remote monitoring. Two IMU units are placed on thorax and abdomen to record breathing-related movements, while a third IMU unit records body/trunk motion and is used as reference. The performance of the proposed system was evaluated in terms of long-acquisition-platform reliability showing good performances in terms of duration and data loss amount. The device was preliminarily tested in terms of accuracy in breathing temporal parameter measurement, in static condition, during postural changes, and during slight indoor activities showing favorable comparison against the reference methods (mean error breathing frequency < 5%). Graphical abstract Proof of concept of a wearable, wireless, modular respiratory Holter based on inertial measurement units (IMUS) for the continuous breathing pattern monitoring through the detection of chest wall breathing-related movements.

Assessment of Breathing Parameters Using an Inertial Measurement Unit (IMU)-Based System

Breathing frequency (f_B) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm for breath-by-breath extraction of breathing temporal parameters from chest-wall inclination change signals measured using inertial measurement units. An important step of the processing algorithm is dimension reduction (DR) that allows the extraction of a single respiratory signal starting from 4-component quaternion data. Three different DR methods are proposed and compared in terms of accuracy of breathing temporal parameter estimation, in a group of healthy subjects, considering different breathing patterns and different postures; optoelectronic plethysmography was used as reference system. In this study, we found that the method based on PCA-fusion of the four quaternion components provided the best f_B estimation performance in terms of mean absolute errors (<2 breaths/min), correlation (r > 0.963) and Bland–Altman Analysis, outperforming the other two methods, based on the selection of a single quaternion component, identified on the basis of spectral analysis; particularly, in supine position, results provided by PCA-based method were even better than those obtained with the ideal quaternion component, determined a posteriori as the one providing the minimum estimation error. The proposed algorithm and system were able to successfully reconstruct the respiration-induced movement, and to accurately determine the respiratory rate in an automatic, position-independent manner.

A novel, low cost, wearable contact-based device for breathing frequency monitoring

This paper presents a device for breathing frequency assessment over the long period, based on low cost, wearable inertial units. Performances of the device were evaluated in static conditions on 9 healthy subjects and the estimated parameters were compared to those obtained with an already validated method (Optoelectronic Plethysmography). We obtained good correlation values (R²> 0.88) and low percentage errors (<;5%) for all the time-based parameters extracted.

Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion

Respiratory activity is an essential vital sign of life that can indicate changes in typical breathing patterns and irregular body functions such as asthma and panic attacks. Many times, there is a need to monitor breathing activity while performing day-to-day functions such as standing, bending, trunk stretching or during yoga exercises. A single IMU (inertial measurement unit) can be used in measuring respiratory motion; however, breathing motion data may be influenced by a body trunk movement that occurs while recording respiratory activity. This research employs a pair of wireless, wearable IMU sensors custom-made by the Department of Electrical Engineering at San Diego State University. After appropriate sensor placement for data collection, this research applies principles of robotics, using the Denavit-Hartenberg convention, to extract relative angular motion between the two sensors. One of the obtained relative joint angles in the “Sagittal” plane predominantly yields respiratory activity. An improvised version of the proposed method and wearable, wireless sensors can be suitable to extract respiratory information while performing sports or exercises, as they do not restrict body motion or the choice of location to gather data.

Wearable technology: role in respiratory health and disease

In the future, diagnostic devices will be able to monitor a patient’s physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare’s Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine.

Smart wearable technologies provide unique opportunities for assessing and monitoring respiratory functionhttp://ow.ly/BHXY30cEfBl